Update scores computation

Author: sovrasov

src/python/model_api/models/keypoint_detection.py

@@ -141,23 +141,31 @@ def _decode_simcc(
     simcc_y: np.ndarray,
     simcc_split_ratio: float = 2.0,
     apply_softmax: bool = False,
+    decode_beta: float = 150.0,
+    sigma: float | int = 6.0,
 ) -> tuple[np.ndarray, np.ndarray]:
     """Decodes keypoint coordinates from SimCC representations. The decoded coordinates are in the input image space.
 
     Args:
         simcc_x (np.ndarray): SimCC label for x-axis
         simcc_y (np.ndarray): SimCC label for y-axis
         simcc_split_ratio (float): The ratio of the label size to the input size.
-        apply_softmax (bool): whether to apply softmax on the heatmap.
+        apply_softmax (bool): whether to apply softmax during scores generation.
             Defaults to False.
+        decode_beta (float): The beta value for decoding scores with softmax. Defaults
+            to 150.0.
+        sigma (float | int): The sigma value in the Gaussian SimCC
+            label. Defaults to 6.0
 
     Returns:
         tuple:
         - keypoints (np.ndarray): Decoded coordinates in shape (N, K, D)
         - scores (np.ndarray): The keypoint scores in shape (N, K).
             It usually represents the confidence of the keypoint prediction
     """
-    keypoints, scores = _get_simcc_maximum(simcc_x, simcc_y, apply_softmax)
+    keypoints, scores = _get_simcc_maximum(simcc_x, simcc_y)
+    if apply_softmax:
+        _, scores = _get_simcc_maximum(decode_beta * sigma * simcc_x, decode_beta * sigma * simcc_y, apply_softmax)
 
     # Unsqueeze the instance dimension for single-instance results
     if keypoints.ndim == 2: